Signal compression is crucial for resource-constrained wireless neural recording applications with limited data bandwidth, and Compressed Sensing (CS) has successfully demonstrated its potential in this field. However, the conventional CS approaches rely on data-dependent and computationally intensive dictionary learning processes to find out the sparse representation of neural signals, and dictionary re-training is inevitable during real experiments. This paper proposes a training-free CS approach for wireless neural recording. By adopting the analysis model to enforce the signal sparsity and constructing a multi-order difference matrix as the analysis operator, it avoids the dictionary learning procedure and reduces the need for previously acquired data and computational complexity. In addition, a group weighted analysis 11-minimization method is developed to recover the neural signals. Experimental results reveal that the proposed approach outperforms the state-of-the-art CS methods for wireless neural recording.
|Original language||English (US)|
|Title of host publication||Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - 2016|
|Event||12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016 - Shanghai, China|
Duration: Oct 17 2016 → Oct 19 2016
|Name||Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016|
|Other||12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016|
|Period||10/17/16 → 10/19/16|
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China under Grants 51578189 and 61401303.
© 2016 IEEE.